# Compose router, evaluator, and handoff workflows over MCP servers with mcp-agent > Connect LLMs to MCP servers through composable patterns like router, evaluator-optimizer, and orchestrator flows without hand-managing server lifecycles. - Skill: `agentskillexchange/compose-router-evaluator-and-handoff-workflows-over-mcp-serv` (Agent Skill) - Install (CLI): `npx skillmds@latest add agentskillexchange/compose-router-evaluator-and-handoff-workflows-over-mcp-serv` - Raw SKILL.md: https://api.skillmd.com/api/skills/agentskillexchange/compose-router-evaluator-and-handoff-workflows-over-mcp-serv/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: agentskillexchange (https://skillmd.com/u/agentskillexchange) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/agentskillexchange/compose-router-evaluator-and-handoff-workflows-over-mcp-serv --- # Compose router, evaluator, and handoff workflows over MCP servers with mcp-agent Connect LLMs to MCP servers through composable patterns like router, evaluator-optimizer, and orchestrator flows without hand-managing server lifecycles. ## Prerequisites Python runtime, MCP servers, LLM API key, optional Temporal for durable workflows ## Installation Use the upstream install or setup path that matches your environment: - uv init - uv add "mcp-agent[openai]" - uv run main.py - uv add "mcp-agent" Requirements and caveats from upstream: - python - We recommend using [uv](https://docs.astral.sh/uv/) to manage your Python projects (uv init). Basic usage or getting-started notes: - - **Minimal example** - async with app.run(): - Source: https://github.com/lastmile-ai/mcp-agent - Extracted from upstream docs: https://raw.githubusercontent.com/lastmile-ai/mcp-agent/HEAD/README.md ## Documentation - https://docs.mcp-agent.com/ ## Source - [Agent Skill Exchange](https://agentskillexchange.com/skills/compose-router-evaluator-and-handoff-workflows-over-mcp-servers-with-mcp-agent/)